

'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class       SCREAMING_SNAKE_CASE       (				lowercase_       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					int     =					['''image_processor''', '''tokenizer''']
      SCREAMING_SNAKE_CASE__			:					Union[str, Any]     =					'''LayoutLMv3ImageProcessor'''
      SCREAMING_SNAKE_CASE__			:					int     =					('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')




      def __init__(       self			:		Tuple  ,       snake_case			:		List[str]=None  ,       snake_case			:		Dict=None  ,       **snake_case			:		Optional[int] ):



          """simple docstring"""





          _snake_case			:			Optional[Any]				       =		None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'  ,       _A  ,       )
              _snake_case			:			Optional[int]				       =		kwargs.pop('feature_extractor' )

          _snake_case			:			Optional[Any]				       =		image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.' )
          if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.' )

          super().__init__(_A  ,       _A )




      def __call__(       self			:		Tuple  ,       snake_case			:		List[Any]  ,       snake_case			:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,       snake_case			:		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  ,       snake_case			:		Union[List[List[int]], List[List[List[int]]]] = None  ,       snake_case			:		Optional[Union[List[int], List[List[int]]]] = None  ,       snake_case			:		bool = True  ,       snake_case			:		Union[bool, str, PaddingStrategy] = False  ,       snake_case			:		Union[bool, str, TruncationStrategy] = None  ,       snake_case			:		Optional[int] = None  ,       snake_case			:		int = 0  ,       snake_case			:		Optional[int] = None  ,       snake_case			:		Optional[bool] = None  ,       snake_case			:		Optional[bool] = None  ,       snake_case			:		bool = False  ,       snake_case			:		bool = False  ,       snake_case			:		bool = False  ,       snake_case			:		bool = False  ,       snake_case			:		bool = True  ,       snake_case			:		Optional[Union[str, TensorType]] = None  ,       **snake_case			:		List[Any]  ,       ):



          """simple docstring"""





          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.' )

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )

          # first, apply the image processor
          _snake_case			:			Optional[int]				       =		self.image_processor(images=_A  ,       return_tensors=_A )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(_A  ,       _A ):
                  _snake_case			:			str				       =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
              _snake_case			:			List[str]				       =		features['''words''']

          _snake_case			:			int				       =		self.tokenizer(
              text=text if text is not None else features['words']  ,       text_pair=text_pair if text_pair is not None else None  ,       boxes=boxes if boxes is not None else features['boxes']  ,       word_labels=_A  ,       add_special_tokens=_A  ,       padding=_A  ,       truncation=_A  ,       max_length=_A  ,       stride=_A  ,       pad_to_multiple_of=_A  ,       return_token_type_ids=_A  ,       return_attention_mask=_A  ,       return_overflowing_tokens=_A  ,       return_special_tokens_mask=_A  ,       return_offsets_mapping=_A  ,       return_length=_A  ,       verbose=_A  ,       return_tensors=_A  ,       **_A  ,       )

          # add pixel values
          _snake_case			:			Optional[Any]				       =		features.pop('pixel_values' )
          if return_overflowing_tokens is True:
              _snake_case			:			Union[str, Any]				       =		self.get_overflowing_images(_A  ,       encoded_inputs['overflow_to_sample_mapping'] )
          _snake_case			:			Union[str, Any]				       =		images

          return encoded_inputs




      def 						__UpperCAmelCase      (       self			:		str  ,       snake_case			:		Optional[Any]  ,       snake_case			:		Any ):



          """simple docstring"""





          _snake_case			:			Optional[int]				       =		[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx] )

          if len(_A ) != len(_A ):
              raise ValueError(
                  'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                  F""" {len(_A )} and {len(_A )}""" )

          return images_with_overflow




      def 						__UpperCAmelCase      (       self			:		Dict  ,       *snake_case			:		Dict  ,       **snake_case			:		Optional[Any] ):



          """simple docstring"""





          return self.tokenizer.batch_decode(*_A  ,       **_A )




      def 						__UpperCAmelCase      (       self			:		Any  ,       *snake_case			:		str  ,       **snake_case			:		Tuple ):



          """simple docstring"""





          return self.tokenizer.decode(*_A  ,       **_A )




      @property
      def 						__UpperCAmelCase      (       self			:		Optional[int] ):



          """simple docstring"""





          return ["input_ids", "bbox", "attention_mask", "pixel_values"]




      @property
      def 						__UpperCAmelCase      (       self			:		Dict ):



          """simple docstring"""





          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,       _A  ,       )
          return self.image_processor_class




      @property
      def 						__UpperCAmelCase      (       self			:		Optional[int] ):



          """simple docstring"""





          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,       _A  ,       )
          return self.image_processor











'''simple docstring'''





_UpperCamelCase						:						Dict  =					range(2, 20 + 1)
_UpperCamelCase						:						str  =					[10**k for k in range(ks[-1] + 1)]
_UpperCamelCase						:						dict[int, dict[int, list[list[int]]]]  =					{}
def __UpperCAmelCase   (					A					:			List[Any]			,       A					:			str			,       A					:			Union[str, Any]			,       A					:			Union[str, Any]		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Optional[Any]          =       sum(a_i[j] for j in range(A			,       len(A		)		)		)
			UpperCAmelCase_      :       Union[str, Any]          =       sum(a_i[j] * base[j] for j in range(min(len(A		)			,       A		)		)		)

			UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       0, 0
			UpperCAmelCase_      :       str          =       n - i

			UpperCAmelCase_      :       Union[str, Any]          =       memo.get(A		)

			if sub_memo is not None:
						UpperCAmelCase_      :       Dict          =       sub_memo.get(A		)

						if jumps is not None and len(A		) > 0:
									# find and make the largest jump without going over
									UpperCAmelCase_      :       str          =       -1
									for _k in range(len(A		) - 1			,       -1			,       -1		):
												if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
															UpperCAmelCase_      :       Any          =       _k
															break

									if max_jump >= 0:
												UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       jumps[max_jump]
												# since the difference between jumps is cached, add c
												UpperCAmelCase_      :       Union[str, Any]          =       diff + c
												for j in range(min(A			,       len(A		)		)		):
															UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       divmod(A			,       1_0		)
												if new_c > 0:
															add(A			,       A			,       A		)

						else:
									UpperCAmelCase_      :       Tuple          =       []
			else:
						UpperCAmelCase_      :       List[str]          =       {c: []}
						UpperCAmelCase_      :       List[str]          =       sub_memo

			if dn >= max_dn or c + diff >= base[k]:
						return diff, dn

			if k > ks[0]:
						while True:
									# keep doing smaller jumps
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[Any]          =       next_term(A			,       k - 1			,       i + dn			,       A		)
									diff += _diff
									dn += terms_jumped

									if dn >= max_dn or c + diff >= base[k]:
												break
			else:
						# would be too small a jump, just compute sequential terms instead
						UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       compute(A			,       A			,       i + dn			,       A		)
						diff += _diff
						dn += terms_jumped

			UpperCAmelCase_      :       List[str]          =       sub_memo[c]

			# keep jumps sorted by # of terms skipped
			UpperCAmelCase_      :       List[Any]          =       0
			while j < len(A		):
						if jumps[j][1] > dn:
									break
						j += 1

			# cache the jump for this value digitsum(b) and c
			sub_memo[c].insert(A			,       (diff, dn, k)		)
			return (diff, dn)
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Dict			,       A					:			List[str]			,       A					:			Union[str, Any]		)    ->   str:
			if i >= n:
						return 0, i
			if k > len(A		):
						a_i.extend([0 for _ in range(k - len(A		)		)]		)

			# note: a_i -> b * 10^k + c
			# ds_b -> digitsum(b)
			# ds_c -> digitsum(c)
			UpperCAmelCase_      :       int          =       i
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       0, 0, 0
			for j in range(len(A		)		):
						if j >= k:
									ds_b += a_i[j]
						else:
									ds_c += a_i[j]

			while i < n:
						i += 1
						UpperCAmelCase_      :       Optional[int]          =       ds_c + ds_b
						diff += addend
						UpperCAmelCase_      :       Any          =       0
						for j in range(A		):
									UpperCAmelCase_      :       Dict          =       a_i[j] + addend
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       divmod(A			,       1_0		)

									ds_c += a_i[j]

						if addend > 0:
									break

			if addend > 0:
						add(A			,       A			,       A		)
			return diff, i - start_i
def __UpperCAmelCase   (					A					:			Dict			,       A					:			Union[str, Any]			,       A					:			str		)    ->   List[Any]:
			for j in range(A			,       len(A		)		):
						UpperCAmelCase_      :       List[str]          =       digits[j] + addend
						if s >= 1_0:
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       divmod(A			,       1_0		)
									UpperCAmelCase_      :       str          =       addend // 1_0 + quotient
						else:
									UpperCAmelCase_      :       Any          =       s
									UpperCAmelCase_      :       Any          =       addend // 1_0

						if addend == 0:
									break

			while addend > 0:
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       divmod(A			,       1_0		)
						digits.append(A		)
def __UpperCAmelCase   (					A					:			int = 1_0**1_5		)    ->   int:
			UpperCAmelCase_      :       Any          =       [1]
			UpperCAmelCase_      :       Union[str, Any]          =       1
			UpperCAmelCase_      :       List[Any]          =       0
			while True:
						UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       next_term(A			,       2_0			,       i + dn			,       A		)
						dn += terms_jumped
						if dn == n - i:
									break

			UpperCAmelCase_      :       Any          =       0
			for j in range(len(A		)		):
						a_n += digits[j] * 1_0**j
			return a_n


if __name__ == "__main__":
						print(f'''{solution() = }''')


